Hello everyone,
I am looking for frequency (fft magnitude) peaks for specific activity.
In order to confirm my theory, I have performed experiments in many
different situations. So, what I need to do is to get specific peaks for
my desired activities. Once I confirm those peaks do not happen in any
other situation(noise, power supply ripple..) my theory is correct.
So now, I have a hard time how to distinguish those peaks.
Is there any specific method (algorithm) that exists, or should I just
look for unions, and differences in my data.
Hope I explained enough,
wg

ana wrote:> Hello everyone,>> I am looking for frequency (fft magnitude) peaks for specific activity.> In order to confirm my theory, I have performed experiments in many> different situations. So, what I need to do is to get specific peaks for> my desired activities. Once I confirm those peaks do not happen in any> other situation(noise, power supply ripple..) my theory is correct.>
Hi,
just for clarification: Are you looking for a way to find the local
maxima of a two dimensional curve, consisting of measured values? Or...
- ... in more than two dimensions?
- ... with constraints regarding the detection of maxima?
- ... under hard (time/space/whatever) conditions?
- ... using interpolation, to find the "real" value?
- ... do you want to skip the fft?
- ... ?
> So now, I have a hard time how to distinguish those peaks.
Umm, of course the most simple algorithm to find a maximum in 2d may be:

if (x[n] > x[n-1]) && (x[n] > x[n+1]) then
print "maximum found."

It runs in situ and in linear time, so there is basically nothing wrong
with it.
When the problem becomes more complicated there may be a bunch of
methods you can try, depending on the concrete constraints; e.g.
- using gradients
- a linear programming solver
- the wide field of genetic programming
- mathmatical methods like Lagrange multipliers
- and surely a lot more...